DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claimed 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “transmitting the image to the user device for display.” Preceding such recitation of “the image” are multiple recitations of various images thus introducing a lack of clarity with respect to which previously recited image “the image” refers. For examination purposes, “the image” is interpreted as a modified version of “the identified image.”
Claims 11 and 16 include substantially the same issue as claim 1 and are rejected for substantially the same reasons.
Claims 2-10, 12-15, and 17-20 inherit the deficiencies of their respective independent claims without providing sufficient clarifying language and thus are also rejected. Examiner suggests amending the claims such that each distinct “image(s)” recited utilizes unique nomenclature.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites storing images; identifying a target item; identifying a location of the target item within the environment based on item data associated with the target item and environment map data describing the environment; selecting an image depicting the item at the location within the environment based on the environment map data and the location data associated with each of the plurality of images; identifying a portion of the identified image that depicts the target item by applying a trained model to the identified image; modifying the identified portion of the identified image to highlight the target item; and displaying the image, which is an abstract idea reasonably categorized as a certain method of organizing human (i.e., managing personal behavior by following rules/instructions).
The additional elements unencompassed by the abstract idea include a user device, a machine-learning model, and a camera and location sensor which collected data prior to the actively recited method claims. These additional elements fail to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claims 2-4 describe input information and thus further describe the abstract idea. The additional elements unencompassed by the abstract idea include a user training a machine-learning model utilizing input information. These additional elements fail to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f). The claims do not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claim 5 recites inputting an identifier of the target item and the plurality of images; receiving a subset of the plurality of images depicting the target item; identifying a plurality of locations, wherein each of the plurality of locations is associated with one of the subset of images; and identifying the location of the target item based on an aggregation of the plurality of locations, which further describes the abstract idea.
The additional elements unencompassed by the abstract idea include a user device, a machine-learning model, and a camera and location sensor which collected data prior to the actively recited method claims. These additional elements fail to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claims 6-10 describe information and the receipt, evaluation, and output thereof and thus further describe the abstract idea.
Claims 11-20 are directed to substantially the same abstract idea as their corresponding method claims and are rejected for substantially the same reasons.
In addition to the additional elements addressed in claim 1, the claims add a processor and computer-readable storage medium. These additional elements fail to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 5-12, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Medina et al. (US 20190087772 A1) in view of Bassemir et al. (US 20140195374 A1).
As per claim 1, Medina discloses a method comprising:
storing a plurality of images depicting items within an environment, each image captured by a camera coupled to a shopping cart in the environment and associated with location data captured by a location sensor of the corresponding shopping cart, wherein each image was captured less than a threshold amount of time from a current time (The computer is configured to capture inventory images from the at least one imaging sensor, detect inventory by comparing captured inventory images with stored inventory images, determine inventory information, Abstract);
identifying a target item associated with a user device, wherein the user device is located within the environment (As another example, the robot 1 may capture an image without flash to identify the type of a reflective item, then capture other images with flash to identify the number of those items, [0066], Fig. 1);
identifying a location of the target item within the environment based on item data associated with the target item and environment map data describing the environment (This height and orientation may be determined by aisle size, product size, inventory location, lighting conditions, or other factors, [0060]);
selecting, from the plurality of images, an image depicting the item at the location within the environment based on the environment map data and the location data associated with each of the plurality of images (The robot 1 may be programmed to navigate through specific waypoints 2 in the store. Alternatively, the robot 1 may determine its own waypoints 2. The robot 1 may collect sensor data, such as images, at each waypoint 2, and may attempt to collect sensor data in precisely the same location in relation to each waypoint 2 as possible, [0052], Fig. 1);
identifying a portion of the identified image that depicts the target item by applying a machine-learning model to the identified image, wherein the machine-learning model is trained to identify portions of images that depict items (Relative to FIGS. 1-4, once an image has been captured as shown in box 400, it may be processed using initial post-processing, computer vision, neural network, machine learning, or deep learning techniques, [0071], Fig. 4).
Medina does not explicitly disclose, however, Bassemir discloses
modifying the identified portion of the identified image to highlight the target item (Bassemir Fig. 7, [0047].); and
transmitting the image to the user device for display (Bassemir Fig. 7, [0047].).
Both Medina and Bassemir are directed to managing inventory. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Bassemir’s features to Medina with the motivation to more efficiently locate items. (Bassemir [0004]).
Furthermore, all of the claimed elements were known in the prior art of Medina and Bassemir and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
As per claim 2, Medina in view of Bassemir discloses the limitations of claim 1 as discussed above. Medina further discloses wherein the machine-learning model is trained on training images, a portion of each training image labeled with an item depicted in the portion of the training image, the method further comprising: receiving, from the machine-learning model, the portion of the identified image (The computer 640 may initially learn inventory characteristics by applying machine learning techniques to a set of training images designed to teach the computer 640, [0092], Fig. 6).
As per claim 5, Medina in view of Bassemir discloses the limitations of claim 1 as discussed above. Medina further discloses
inputting an identifier of the target item and the plurality of images to a machine-learning model, the machine-learning model trained on identifiers of items in the environment labeled with one or more images depicting a respective item, the images captured by cameras coupled to shopping carts in the environment (Relative to FIGS. 1-4, once an image has been captured as shown in box 400, it may be processed using initial post-processing, computer vision, neural network, machine learning, or deep learning techniques, [0071], Fig. 4);
receiving, from the machine-learning model, a subset of the plurality of images depicting the target item (For example, the robot 1 may confirm its location within the facility by comparing expected image data with actual image data at a waypoint 2. The robot 1 may expect to capture images of a certain type of product at one waypoint 2, and it may compare the captured images to expected images, [0054], Fig. 1);
identifying a plurality of locations, wherein each of the plurality of locations is associated with one of the subset of images, each location determined based on location data captured by a respective camera coupled to a respective shopping cart that captured the respective image (Relative to FIGS. 1-3, as the robot 1 travels between waypoints 2, it may perform the process of capturing inventory images 7 using the imaging sensor 20. Depending on the design, in implementation, the imaging sensor 20 may be a camera or camera system, [0060], Fig. 1-3); and
identifying the location of the target item based on an aggregation of the plurality of locations (It may also distribute and update the more intelligent components of its network with the additional training it has gathered from the stream of aggregated information across the network, such that they can be taken advantage of locally by those devices, [0154]).
As per claim 6, Medina in view of Bassemir discloses the limitations of claim 1 as discussed above. Medina further discloses wherein identifying the target item comprises identifying a next item to be collected by the user by: receiving an ordered list of items stored at the device, wherein the order of the list is indicative of an order for retrieving items in the list within the environment; identifying that one of more of the items in the list have been retrieved by the user based on sensor data; and identifying a next item for retrieval based on the one or more of the items that have been retrieved and the order of the list, wherein the next item is the item (The associate may also click on or interact with availability boxes 2120, which may open additional menus and lists. For instance, an availability box showing the items available in store may allow the associate to find all of those items. An availability box showing a list of the items not available in the store may allow the associate to backorder those item, [0162], Fig. 21).
As per claim 7, Medina in view of Bassemir discloses the limitations of claim 6 as discussed above. Medina further discloses wherein the sensor data includes one or more of an interaction with a touchscreen display, a radio frequency identification (RFID) detection associated with the item, or an image of the item in a shopping cart associated with the user (In one example, the sensors 1010 may be
RFID scanners capable of detecting RFID devices located within items on the shelves. The computer 1008 may use simultaneous location and mapping (SLAM) techniques in conjunction with the RFID tag data to know in real-time its exact location within the environment, [0110], Fig. 10).
As per claim 8, Medina in view of Bassemir discloses the limitations of claim 1 as discussed above. Medina further discloses wherein identifying the target item associated with the user device comprises: requesting, from the device, content being presented at the device; and identifying the target item in response to determining that the content describes the target item (As shown in box 438, a heuristic identification process may be used to identify an item by its price label. The heuristic process compares previous images captured under similar conditions, such as location in the store or distance from the item, to new images, comparing detected features and other data, [0076], Fig. 4).
As per claim 9, Medina in view of Bassemir discloses the limitations of claim 1 as discussed above. Medina further discloses wherein transmitting the image to the device for display to the user is responsive to determining, based on location data received from the device, that the device is within a threshold area of the location in the environment (In one example, inventory information with a confidence
level below a threshold may direct the robot 1 to capture additional images for the subject inventory. The robot 1 may return to the portion of the facility where the image was taken and take another image, [0085], Fig. 1).
As per claim 10, Medina in view of Bassemir discloses the limitations of claim 1 as discussed above. Medina may not explicitly disclose, however Bassemir discloses wherein modifying the identified portion of the identified image to highlight the target item comprises adding a border around the identified portion in the identified image (Bassemir Fig. 7, [0047].).
Both Medina and Bassemir are directed to managing inventory. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Bassemir’s features to Medina with the motivation to more efficiently locate items. (Bassemir [0004]).
Furthermore, all of the claimed elements were known in the prior art of Medina and Bassemir and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
As per claim 11, Medina discloses a non-transitory computer-readable storage medium storing instructions that, when executed, cause a processor to perform steps comprising:
storing a plurality of images depicting items within an environment, each image captured by a camera coupled to a shopping cart in the environment and associated with location data captured by a location sensor of the corresponding shopping cart, wherein each image was captured less than a threshold amount of time from a current time (The computer is configured to capture inventory images from the at least one imaging sensor, detect inventory by comparing captured inventory images with stored inventory images, determine inventory information, Abstract);
identifying a target item associated with a user device, wherein the user device is located within the environment (As another example, the robot 1 may capture an image without flash to identify the type of a reflective item, then capture other images with flash to identify the number of those items, [0066], Fig. 1);
identifying a location of the target item within the environment based on item data associated with the target item and environment map data describing the environment (This height and orientation may be determined by aisle size, product size, inventory location, lighting conditions, or other factors, [0060]);
selecting, from the plurality of images, an image depicting the item at the location within the environment based on the environment map data and the location data associated with each of the plurality of images (The robot 1 may be programmed to navigate through specific waypoints 2 in the store. Alternatively, the robot 1 may determine its own waypoints 2. The robot 1 may collect sensor data, such as images, at each waypoint 2, and may attempt to collect sensor data in precisely the same location in relation to each waypoint 2 as possible, [0052], Fig. 1);
identifying a portion of the identified image that depicts the target item by applying a machine-learning model to the identified image, wherein the machine-learning model is trained to identify portions of images that depict items (Relative to FIGS. 1-4, once an image has been captured as shown in box 400, it may be processed using initial post-processing, computer vision, neural network, machine learning, or deep learning techniques, [0071], Fig. 4).
Medina does not explicitly disclose, however, Bassemir discloses
modifying the identified portion of the identified image to highlight the target item (Bassemir Fig. 7, [0047].); and
transmitting the image to the user device for display (Bassemir Fig. 7, [0047].).
Both Medina and Bassemir are directed to managing inventory. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Bassemir’s features to Medina with the motivation to more efficiently locate items. (Bassemir [0004]).
Furthermore, all of the claimed elements were known in the prior art of Medina and Bassemir and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
As per claim 12, Medina in view of Bassemir discloses the limitations of claim 11 as discussed above. Medina further discloses wherein the machine-learning model is trained on training images, a portion of each training image labeled with an item depicted in the portion of the training image, the method further comprising: receiving, from the machine-learning model, the portion of the identified image (The computer 640 may initially learn inventory characteristics by applying machine learning techniques to a set of training images designed to teach the computer 640, [0092], Fig. 6).
As per claim 15, Medina in view of Bassemir discloses the limitations of claim 11 as discussed above. Medina further discloses
inputting an identifier of the target item and the plurality of images to a machine-learning model, the machine-learning model trained on identifiers of items in the environment labeled with one or more images depicting a respective item, the images captured by cameras coupled to shopping carts in the environment (Relative to FIGS. 1-4, once an image has been captured as shown in box 400, it may be processed using initial post-processing, computer vision, neural network, machine learning, or deep learning techniques, [0071], Fig. 4);
receiving, from the machine-learning model, a subset of the plurality of images depicting the target item (For example, the robot 1 may confirm its location within the facility by comparing expected image data with actual image data at a waypoint 2. The robot 1 may expect to capture images of a certain type of product at one waypoint 2, and it may compare the captured images to expected images, [0054], Fig. 1);
identifying a plurality of locations, wherein each of the plurality of locations is associated with one of the subset of images, each location determined based on location data captured by a respective camera coupled to a respective shopping cart that captured the respective image (Relative to FIGS. 1-3, as the robot 1 travels between waypoints 2, it may perform the process of capturing inventory images 7 using the imaging sensor 20. Depending on the design, in implementation, the imaging sensor 20 may be a camera or camera system, [0060], Fig. 1-3); and
identifying the location of the target item based on an aggregation of the plurality of locations (It may also distribute and update the more intelligent components of its network with the additional training it has gathered from the stream of aggregated information across the network, such that they can be taken advantage of locally by those devices, [0154]).
As per claim 16, Medina discloses a system comprising a processor and a non-transitory computer-readable storage medium storing instructions that, when executed, cause a processor to perform steps comprising:
storing a plurality of images depicting items within an environment, each image captured by a camera coupled to a shopping cart in the environment and associated with location data captured by a location sensor of the corresponding shopping cart, wherein each image was captured less than a threshold amount of time from a current time (The computer is configured to capture inventory images from the at least one imaging sensor, detect inventory by comparing captured inventory images with stored inventory images, determine inventory information, Abstract);
identifying a target item associated with a user device, wherein the user device is located within the environment (As another example, the robot 1 may capture an image without flash to identify the type of a reflective item, then capture other images with flash to identify the number of those items, [0066], Fig. 1);
identifying a location of the target item within the environment based on item data associated with the target item and environment map data describing the environment (This height and orientation may be determined by aisle size, product size, inventory location, lighting conditions, or other factors, [0060]);
selecting, from the plurality of images, an image depicting the item at the location within the environment based on the environment map data and the location data associated with each of the plurality of images (The robot 1 may be programmed to navigate through specific waypoints 2 in the store. Alternatively, the robot 1 may determine its own waypoints 2. The robot 1 may collect sensor data, such as images, at each waypoint 2, and may attempt to collect sensor data in precisely the same location in relation to each waypoint 2 as possible, [0052], Fig. 1);
identifying a portion of the identified image that depicts the target item by applying a machine-learning model to the identified image, wherein the machine-learning model is trained to identify portions of images that depict items (Relative to FIGS. 1-4, once an image has been captured as shown in box 400, it may be processed using initial post-processing, computer vision, neural network, machine learning, or deep learning techniques, [0071], Fig. 4).
Medina does not explicitly disclose, however, Bassemir discloses
modifying the identified portion of the identified image to highlight the target item (Bassemir Fig. 7, [0047].); and
transmitting the image to the user device for display (Bassemir Fig. 7, [0047].).
Both Medina and Bassemir are directed to managing inventory. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Bassemir’s features to Medina with the motivation to more efficiently locate items. (Bassemir [0004]).
Furthermore, all of the claimed elements were known in the prior art of Medina and Bassemir and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
As per claim 17, Medina in view of Bassemir discloses the limitations of claim 11 as discussed above. Medina further discloses wherein the machine-learning model is trained on training images, a portion of each training image labeled with an item depicted in the portion of the training image, the steps further comprising: receiving, from the machine-learning model, the portion of the identified image (The computer 640 may initially learn inventory characteristics by applying machine learning techniques to a set of training images designed to teach the computer 640, [0092], Fig. 6).
As per claim 20, Medina in view of Bassemir discloses the limitations of claim 16 as discussed above. Medina further discloses
inputting an identifier of the target item and the plurality of images to a machine-learning model, the machine-learning model trained on identifiers of items in the environment labeled with one or more images depicting a respective item, the images captured by cameras coupled to shopping carts in the environment (Relative to FIGS. 1-4, once an image has been captured as shown in box 400, it may be processed using initial post-processing, computer vision, neural network, machine learning, or deep learning techniques, [0071], Fig. 4);
receiving, from the machine-learning model, a subset of the plurality of images depicting the target item (For example, the robot 1 may confirm its location within the facility by comparing expected image data with actual image data at a waypoint 2. The robot 1 may expect to capture images of a certain type of product at one waypoint 2, and it may compare the captured images to expected images, [0054], Fig. 1);
identifying a plurality of locations, wherein each of the plurality of locations is associated with one of the subset of images, each location determined based on location data captured by a respective camera coupled to a respective shopping cart that captured the respective image (Relative to FIGS. 1-3, as the robot 1 travels between waypoints 2, it may perform the process of capturing inventory images 7 using the imaging sensor 20. Depending on the design, in implementation, the imaging sensor 20 may be a camera or camera system, [0060], Fig. 1-3); and
identifying the location of the target item based on an aggregation of the plurality of locations (It may also distribute and update the more intelligent components of its network with the additional training it has gathered from the stream of aggregated information across the network, such that they can be taken advantage of locally by those devices, [0154]).
Claims 3, 4, 13, 14, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Medina et al. (US 20190087772 A1) in view of Bassemir et al. (US 20140195374 A1) and Desai (US 20180114098 A1).
As per claim 3, Medina in view of Bassemir discloses the limitations of claim 2 as discussed above. Medina may not explicitly disclose, however, Desai discloses wherein each image is further labeled with a rating of the image input by a user of the corresponding shopping cart via the device, the method further comprising: causing the device to display one or more interactive elements configured to receive a rating of the image from the user (Desai e.g., [0044]. Labels presented to users who select correct labels. Model trained based on this user input.).
Medina, Bassemir, and Desai each involve object recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Desai’s features to Medina and Bassemir as the application of a known training technique to a known process ready for improvement to yield predictable results.
As per claim 4, Medina in view of Bassemir discloses the limitations of claim 3 as discussed above. Medina may not explicitly disclose, however, Desai discloses training the machine-learning model on the image labeled with the rating from the user (Desai e.g., [0044]. Labels presented to users who select correct labels. Model trained based on this user input.).
Medina, Bassemir, and Desai each involve object recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Desai’s features to Medina and Bassemir as the application of a known training technique to a known process ready for improvement to yield predictable results.
As per claim 13, Medina in view of Bassemir discloses the limitations of claim 12 as discussed above. Medina may not explicitly disclose, however, Desai discloses wherein each image is further labeled with a rating of the image input by a user of the corresponding shopping cart via the device, the method further comprising: causing the device to display one or more interactive elements configured to receive a rating of the image from the user (Desai e.g., [0044]. Labels presented to users who select correct labels. Model trained based on this user input.).
Medina, Bassemir, and Desai each involve object recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Desai’s features to Medina and Bassemir as the application of a known training technique to a known process ready for improvement to yield predictable results.
As per claim 14, Medina in view of Bassemir discloses the limitations of claim 13 as discussed above. Medina may not explicitly disclose, however, Desai discloses training the machine-learning model on the image labeled with the rating from the user (Desai e.g., [0044]. Labels presented to users who select correct labels. Model trained based on this user input.).
Medina, Bassemir, and Desai each involve object recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Desai’s features to Medina and Bassemir as the application of a known training technique to a known process ready for improvement to yield predictable results.
As per claim 18, Medina in view of Bassemir discloses the limitations of claim 17 as discussed above. Medina may not explicitly disclose, however, Desai discloses wherein each image is further labeled with a rating of the image input by a user of the corresponding shopping cart via the device, the method further comprising: causing the device to display one or more interactive elements configured to receive a rating of the image from the user (Desai e.g., [0044]. Labels presented to users who select correct labels. Model trained based on this user input.).
Medina, Bassemir, and Desai each involve object recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Desai’s features to Medina and Bassemir as the application of a known training technique to a known process ready for improvement to yield predictable results.
As per claim 19, Medina in view of Bassemir discloses the limitations of claim 18 as discussed above. Medina may not explicitly disclose, however, Desai discloses training the machine-learning model on the image labeled with the rating from the user (Desai e.g., [0044]. Labels presented to users who select correct labels. Model trained based on this user input.).
Medina, Bassemir, and Desai each involve object recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add Desai’s features to Medina and Bassemir as the application of a known training technique to a known process ready for improvement to yield predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
Applicant's disclosure. Please see PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFF ZIMMERMAN whose telephone number is (571)272-4602. The examiner can normally be reached Monday - Thursday 6:00 am - 2:00 pm.
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JEFF ZIMMERMAN
Supervisory Patent Examiner
Art Unit 3628
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
Prosecution Insights